Does your organization make optimal use of IIoT data?

With the help of the Industrial Internet of Things (IIoT) it is possible for manufacturers to make their companies run more efficiently. But how do you ensure that you analyze IIoT data correctly and ensure digital transformation and progress?

An example: the Bosch Automotive Diesel factory uses IIoT and data to optimize production processes. This German-Chinese joint venture produces high-efficiency and low-emission diesel engine parts. To manage production, they have implemented smart sensors that connect all machinery and generate data. The insights generated by these sensors enable the manufacturer to understand and eliminate output losses from a machine and prevent machine failures. This leads to an increase in the uptime of the smart factory. The use of data analysis has led to a 17% increase in output. Decisions are made faster and more efficiently and result in an optimized factory.

The successful use of IIoT data is not a simple task for every company. But how do you know what stage the company is in when it comes to optimally deploying IIoT data? To get an idea of ​​how far you are as a company, you can invest the following four questions within your organization:

1. Does my organization have clear strategic objectives for both the short and the long term?
   For optimal data use, you must have a good idea of how the data is going to be used to improve business processes. This step is crucial because it describes the new functionalities or possibilities that the IIoT project will deliver. In addition, a periodic reassessment must be made during implementation to validate whether the project still delivers the originally expected business objectives and results.
   
2. Do you know which data is collected?
   It is often unclear to companies from which sources data is collected and how the data should communicate with each other. The collected data is unstructured and is stored in a so-called data lake. A data lake is a storage place where a huge amount of raw data is stored until it is needed. When a lot of time and money is invested in the data lake but no return (relevant control information) is achieved, you are dealing with a frozen data lake. This often occurs with companies where there is little knowledge and experience in the field of data.
   
3. Is there in-house knowledge to analyze the data and to be able to draw conclusions from it?
   Sometimes there is little knowledge within a company about the basis of IIoT data: what it actually is, what the benefits are, what infrastructure is needed. It takes a lot of time and resources to implement something that the company does not know how to use. To prevent this from happening, it is good to organize training courses and workshops that teach employees how to deal with data.
   
4. Is the data properly secured?
   Connecting sensors and implementing IIoT offers enormous opportunities but can also lead to security leaks. Is the data sufficiently protected or is there a chance that people outside the company can view the data? Who is in control of the data? What about the private data of employees stored in the database? Ensure that the organization meets the requirements of the General Data Protection Regulation (GDPR). By including security and privacy protection from the start of the design of the IIoT system, you can reduce the risk of infringement. This starts with keeping software and devices up-to-date, by identifying risks, installing patches, testing periodically, logging and ensuring that all work procedures related to system security are followed meticulously.

By answering these questions you gain insight into which phase your company is in and which steps are necessary to make optimal use of the collected data. The white paper “Optimizing your Business with Industrial Internet of Things” contains 15 questions that help you investigate how far your organization is in data analysis. Are you also interested in applications of IIoT and tips for a successful implementation?

Download the whitepaper here.